Opara-Eze Tochi ¶"This is Miya and Kiyo. You never know when someone might need a hug, so Miya tries to be proactive about it. Kiyo stays available for quality control. 13/10 for both" - Image Source
I had the opportunity to experience the entire data analysis process for the data wrangling project on the Udacity Data Analyst Nanodegree, from gathering the data to cleaning and analyzing it to eventually showing trends from the data. The information was gathered from the Twitter account "WeRateDogs," which gives most dogs a rating of at least 10.
WeRateDogs is a Twitter account that rates people's dogs with a humorous comment about the dog. The account was started in 2015 by college student Matt Nelson, and has received international media attention both for its popularity and for the attention drawn to social media copyright law when it was suspended by Twitter for breaking these aforementioned laws - Wikipedia
The denominator of these scores is almost always 10. however, the numerators? frequently more than 10. 11/10, 12/10, 13/10, etc. Why? because "Brent, they're good dogs." Over 9.2 million people follow WeRateDogs as at the time of preparing this report, and it has been featured in international media.
What are we to do with these ratings then? Which dog is the most well-known among dogs has to be the finest common query? Can we find a connection between favorites, ratings, and tweets? Which dogs would have gotten the worse scores? In order to get the answers to these questions, I combed through the WeRateDogs twitter data and conducted an analysis. I was able to create some beautiful visualizations for my study with the aid of Python libraries like Pandas, Matplotlib and Seaborn
2 Let us visually find out which is the most favourite dog stage among the audience of '@WeRateDogs'¶3 Top 10 most popular dog breeds in the first (p1), second (p2) and third (p3) predictions?¶It is critical to conduct an initial analysis to identify any and all data errors with the data set. This analysis aids in the subsequent planning and comprehension of the data collection, as well as the optimization of the data cleaning process.
For more details, check out my GitHub ✌🏾